User-oriented multimedia presentation system for multiple presentation items that each behave as an agent

ABSTRACT

A user-adaptive audio and/or video presentation system contains a background sub-system for presenting audio and/or video items, control for the background subsystem as regarding an actual selection sequence amongst the items according to a user preference pattern, and output means for physically presenting selected items to a user. Each item has a uniformly structured first set of static attribute data and associated second set of weight value data, and a third set of dynamic behaviour data. The dynamic behaviour of the items is collectively governed by behavioral and interactivity prescriptions, so that each respective item represents an autonomous agent. The control includes processing for under influence of the first and second sets of data and as governed by the prescriptions, updating weight and dynamic behaviour parameter data of each item versus other items for subsequently influencing the controlling.

BACKGROUND OF THE INVENTION

The invention relates to a user-adaptive audio and/or video presentationsystem containing a background presentation sub-system for presentingaudio and/or video items, control means for controlling saidpresentation sub-system as regarding an automatically generatedselection sequence amongst said items according to a user preferencepattern, and output means for physically presenting selected items to auser. Present-day delivery systems are quickly growing in terms ofstorage capacity and presentation bandwidth. Vis a vis a more or lessconstant absorption capacity for information flow in humans, thisconfronts the user person with an increasingly complex decision problemfor choosing between various alternative presentations. A non-limitingexample of such system is a jukebox loaded with Compact Discs, thatcould at present contain a hundred discs with some twenty songs each.The user could then choose along various strategies, such as randomamongst the discs and sequential per disc, but this has been found toorestrictive. On the other hand, random selection among all trackspresent is felt as insufficiently coherent.

The selection problem sketched above can occur in various otherenvironments, such as when choosing between a hundred or more concurrentTV channels, that each feature a sequence of items, or in a largecollection of CD Video discs. Similar situations can occur with videoclips or video games. The items may be based on uniform technology suchas in the case of CD records. In a multimedia situation, the items maybe intermixed, such as audio records competing against TV channels, orin the case of simultaneously selecting among still pictures as well asamong audio items for concurrent presentation of the chosen audio plusvideo. Competing items need not have uniform presentations, such as anaudio record versus an interactive audio-plus-video-plus-graphics game.The problem may occur on several hierarchical levels at a time, such aswhen simultaneously selecting among audio tracks as well as among wholeaudio records.

The present inventors have encountered a need for machine implementing asequential choice amongst the items that is both meaningful in view ofthe user's preferences, but on the other hand is sufficientlynon-uniform between different presentation sessions, and thereby givesboth some coherence between presentations that are relatively close intime, but also certain startling effects through differentiating betweensuch presentations and introducing a certain variability betweendifferent sessions.

SUMMARY TO THE INVENTION

Therefore, amongst other things it is an object of the invention toprovide a system as recited above that gives more coherent presentationsequences than random selection alone would produce, but on the otherhand observes more variability than what has been marketed up to now as`favourite track selection`: therein, after programming of a particularsequence, this sequence remains fixed. A particular additional problemof `favourite track selection` is the need for individual programming byeach user, which many users have felt as cumbersome. On the other hand,in `shuffle play` the player itself executes a random selection amongstitems that are physically available; in the view of the presentinvention, a system of this kind has too little coherence in a sequenceof items presented.

According to a first aspect of the invention it is thereto characterizedin that each said item has a uniformly structured first set of staticattribute data and associated second set of weight value data, and alsoa third set of dynamic behaviour parameter data, and said items are withrespect to their dynamic behaviour collectively governed by a set ofbehavioral and interactivity prescriptions, so that each respective itemrepresents a respective autonomous agent, and said control means includeprocessing means for under influence of said first and second sets ofdata and as governed by said prescriptions, updating said weights anddynamic behaviour parameter data of each item in question vis a visother said items for subsequently influencing said controlling. Theformulating as autonomous agents allows for a wide variability ofpresentations, while also retaining a certain coherence betweensuccessive items. The three levels for defining the properties of theitems (attribute, weight and actual behaviour) allow wide applicabilityof the principles explained hereabove.

By itself, earlier developments by the present assignee have led toother agent-based systems, such as disclosed in U.S. Pat. No. 5,418,887(PHB33549), EP 704 077, corresponding U.S. patent application Ser. No.08/418,995 (PHB 33903), EP Application 724 751 corresponding U.S. patentapplication Ser. No. 08/498,289 (PHB33914), EP Application 722 592,corresponding U.S. patent application Ser. No. 08/498,280 (PHB33915),and EP 95201526.1, corresponding U.S. patent application Ser. No.08/655,169 (PHN15336).

Now, the present invention implements the items as autonomous agents.Such autonomous agents contain their own static attribute data anddynamic weight data. A weight indicates the relevance of the associatedstatic attribute. The dynamic behaviour parameters represent the actualbehaviour of the item. The behavioral and interactivity prescriptionsgovern the evolving behaviour, under influence of static and dynamicproperties of the item, and under influence of interactions between therespective items.

Advantageously, said behaviour is metaphored as motion within a finitespace of at least two dimensions. This allows continual evolution of thebehaviour of the items, and thereby lends maximum flexibility thereto.Moreover, it allows an excellent visualization of the behaviour, therebyenabling all kinds of potential fine-tuning by a user, through effectingvarious selections. The behaviour can be visualized on a display screen,either at prototype or high-end machines only, or alternatively, on allmachines that have a display screen.

Advantageously, such system is arranged for forming among said items oneor more temporary association clusters, which forming is conditional tosimilar behaviour amongst the respective item agents in such cluster,and said presentation favours an item pertaining to a most recentlyaccessed said cluster. In particular, the notion of a cluster has beenfound to introduce some structure among the various items, therebygiving a certain amount of coherence in the presentation sequence.

Advantageously, the system has user evaluation input means forselectively influencing said updating at least with respect to saidweight value data for a particular item through an evaluatory physicalinput. In this manner, opinions of an actual user can influence thebehaviour of the agents and thus codetermine the outcome of theselection process.

Advantageously, a system having freeze means for blocking autonomy ofsaid agents, and thus freezing said behaviour as dictated by saidautonomy. For example, the freezing can become effective at the end of apresentation sequence. The same machine may then cater to various userpreference patterns, that could differ between persons, as well asbetween different situations, such as depending on time-of-day. Thefreezed situation could be stored for later reactivation. The inventionmay be used in a network environment, either with broadcastpresentation, or with personalized presentation, such as with earphones.Each user could have his own evaluation mechanism for influencing theagent behaviour.

Advantageously, said presentation sub-system is a storage system forstoring and accessing audio items which are distributed amongst aplurality of physical media instances that each allow collective storageof a plurality of such items. This is a particularly advantageousembodiment. The invention also relates to a method for audio and/orvideo presentations along the principles set out above. Various furtheradvantageous aspects are recited in dependent claims.

BRIEF DESCRIPTION OF THE DRAWING.

These and other aspects and advantages of the invention will beexplained more in detail with reference to the detailed disclosure ofpreferred embodiments hereinafter, and in particular with reference tothe appended Figures that show:

FIG. 1, a block diagram of an exemplary embodiment;

FIG. 2, an exemplary configuration of stored items;

FIG. 3, a sample display of a graphical user interface;

FIG. 4, a typical listening scenario of a system;

FIG. 5, a detailed object diagram of a set of items;

FIG. 6, a detailed object diagram of item behaviour;

FIG. 7, a detailed object diagram of item interaction;

FIG. 8, a detailed object diagram of bookkeeping;

FIG. 9, graphical symbols used in an object diagram;

FIG. 10, an illustration of the parameters Coverage and Precision.

A FEW USER ASPECTS OF THE PRESENT INVENTION

The invention has for the present embodiment been abbreviated as PATS(Personalized Automatic Track Selection). In the terminology of CompactDisc, usually each track corresponds to exactly one song or other audioequivalency. An earlier system for giving users influence on thepresentation sequence in advance of the actual presentation, has beenmarketed as `favourite track selection` (FTS). Therein, the userprograms the disc player to follow a particular sequence amongst thetracks with respect to a particular Compact Disc or other medium. Forplayers that allow a more or less random selection amongst a pluralityof discs, such favourite tracks selection can be implemented both withina particular disc, and also amongst those various discs, in the way of ajukebox. Users have felt the programming a tedious effort. Further, theprogramming is generally linked to the memory of a particular player.Even if the programming sequence would be stored on a writable part ofthe disc, it is fixed. However, users may want to apply the presentinvention in a number of different activities, such as active listening,background music, and personalizing the sequence for use at a particularoccasion, such as giving it as a present or playing it at a party.

The present invention serves user intentions that are not optimallyaddressed by current systems. Examples of such specific intentions thatthe present system tries to accommodate are:

people may enjoy somebody else doing the selecting of music for them,such as disc jockeys or radio programmes.

people like to listen to music that is known to them.

people like a certain coherence between successive music tracks thatform a listening session, such as thematic radio programmes or radiochannels focusing on a particular style.

people appreciate assistance in the selection process when the amount ofavailable music to choose from becomes larger, such as in the case ofmagazines or sampler CD's.

somebody who `knows` the user can influence the selection process bypresenting interesting or unexpected options, such as would be the casefor a sales person or an acquaintance.

the selection of certain music tracks is influenced by the onespreviously listened to.

DETAILED DISCLOSURE OF PREFERRED EMBODIMENTS

FIG. 1 is a block diagram of an exemplary embodiment of the invention,in particular as realized in the context of a Compact Disc player withjukebox functionality. For reasons of clarity, all parts and subsystemshave been shown in fragmentary manner only. Now, a stack 20 of recordsis rotated by means of a drive mechanism symbolized by axis 18. Accessmechanism 22 allows to select among the various records, and for eachrecord among the tracks thereon. If applicable, both read and write ondisc can be allowed, although the invention may in principle functionwith read-only. Player body 24 comprises various subsystems. First,block 28 controls the accessing by access mechanism 22. To this effect,external controls 26 such as user buttons or remote control have beenprovided. Further control and selection signals are produced byprocessing means 34. For simplicity, blocks 28, 30, 34 have been linkedin a chain; it is understood that blocks 34 and 28 may interactimmediately. The mechanical access facility is only one of many feasibleembodiments as well.

Block 30 represents the audio processing; the audio output proper issymbolized by arrow 32, and the output is time-conformingly, so that theuser experiences the resulting speech, music, etcetera as a naturalphenomenon. Of course, fast-forward and other modes can be provided aswell, both for standard output, and with respect to adaptation to theuser's wishes. Moreover, in the case of a (video) game, the presentationneed not follow a uniform temporal sequence; also still pictures couldbe presented. Parallel output of more than one presentation stream wouldbe feasible as well, such as speech combined with background audio froman unconnected track from stack 20. Video and audio can be combined;animation from a game can be combined with audio of a kind preferred bythe user.

Processing means 34 implement the dynamic behaviour of the items asagents. It may be based on conventional hardware, whereas the associatedsoftware will be discussed hereinafter. The software may be resident;the data that relate directly to the items may be received from theaudio record, but also from an external facility such as Internet. Ifreceived from the discs, this is done by an initial scan from all discsof the stack before replay actually starts. Output of the selectingsystem is a sequence of control signals for access controller 28. Ifappropriate, certain control signals, or behaviour data of the items maybe written on the actually accessed disc from stack 20. In particular,this can be done when entering a `freezed` situation.

FIG. 2 shows an exemplary configuration of stored items. In this limitedexample, each row relates to one item, or in the case of CD, a track.The main body 50 of the respective items contains the user information,that in a simple case may be a single song. In another environment, itmay constitute a movie episode, a photograph, and beside userinformation for direct outputting, it may contain program data forcontrolling the user features, such as is feasible in a Compact-DiscInteractive environment. Furthermore, each item in column 40 has anidentifier, such as a number, and if applicable, an alphanumerical name,such as the title of the disc. Each block 42-48 for the item allowsstorage of a static attribute information and a weight quantity for eachattribute, which informations pertain to the content or other aspects ofthe item in question. For music, the attributes may be the title of thesong, the composer, the band, the lead musician's name, a list andcategorization of instruments, item length, recording date, beat, typeor style of the music, and various others. For other types of item otherattributes apply.

The weight informations will generally not be constant. Initially, theweights constitute seed data to enable subsequent behaviour of the itemas an autonomous agent. Subsequently, these weights are updated undercontrol of past presentation data and/or user control signals onsymbolic input 26 in FIG. 1, and are retained in local memory ofprocessing means 34. The behaviour of the items is governed collectivelyby behavioral and interactivity prescriptions, that can be metaphored asthe `equations of motion` of the items; these need not be uniform amongthe various items. If necessary, also the behaviour parameters wouldneed the providing of seed data. In this way, each respective itemconstitutes an autonomous agent. This means that the behaviour of theitems is also subject to external influences.

USAGE OF THE AGENT FEATURE

The agent feature of the present invention uses three sets ofinformation that influence the behaviour of the items for presentation:

a. Each item has static attribute data, that each have a value anddescribe the properties of the item, such as were discussed in thepreceding paragraph. Examples in the case of music are binary attributes(yes/no live recording), nominal attributes (music style), ordinalattributes (such as tempo, distinguished into slow-medium-fast),interval attributes (serial number), ratio attributes (number ofmusicians).

b. Second, each attribute value has a weight, that indicates therelevance of the attribute, as will be elucidated by the description ofthe behaviour principles hereinafter.

c. Third, each item has dynamic behaviour parameters that are privy toeach item, and that represent instantaneous behaviour of each respectiveitem agent, as influenced by the input quantities under #a and #b. Inthe metaphor used herein, these could express instantaneous position andspeed of the item as being mapped on a particle, and the particularfeatures to `observe` or `sense` another agent.

d. Finally, the whole set of agents is governed by behaviourial andinteractivity prescriptions between the agents.

Now, the agents form the population of the database. The primemotivation for each agent would be to achieve a positive appreciation bya user person, which would define a success quantity for the item inquestion. A secondary motivation for an agent is to associate itself toanother agent, with which it has something among the attributes incommon. Now, in the discussion hereinafter, for a particular behaviourto occur, generally one or more other conditional behaviours of theagent itself or of other agents, are a prerequisite. The behaviourhereinafter is restricted to executing an action. A particular agent maybe undertaking various different actions simultaneously. The followingexemplary behaviours are listed:

SenseOther. An agent can sense another agent if the two are within acertain Euclidean distance or within the same sector of the databasespace. For reasons of visualization, the space is hereinafter mapped ona two-dimensional plane, in which the agents move with a non-uniform,and not necessarily steady speed.

ObserveOther. If an agent senses another agent, a similarity measure ofthe other's track to the track of the observing agent is calculated. Theagent will follow under various conditions, that may comprise one ormore of the following:

1) if the similarity exceeds a threshold it will follow;

2) the similarity determines a probability for following, which canoccur if several agents are sensing each other simultaneously;

3) the threshold is determined from earlier observations;

4) the threshold is based on other agents in the cluster of the observedagent, thereby determining which one of the other agents should befollowed.

FollowOther. If an agent decides to follow another agent, it assignsitself to the cluster of the agent followed, which may be either anexisting cluster, or a new one. If an agent decides to stop followinganother agent, it removes itself from the cluster of the latter agent,which may lead to dissolving the cluster in that only isolated agentsremain thereof.

ReObserveOther. If an agent is presently executing a FollowOtherbehaviour, and if within a certain time no new candidate for followingis sensed, the above similarity measure is calculated again. Also thethreshold may be updated, such as under influence of the number and/orbehaviour of the other agents in the cluster. This may effectively leadto terminating of the action of following. This terminating can lead tonew situations: either the old cluster persists with one item less, orthe old cluster is dissolved in that it is reduced to a single item. Theterminating item itself can become either a free item or agent, or maybecome the root agent of a cluster that is split of.

In this respect, FIG. 3 gives a sample display of a graphical userinterface for a prototype embodiment. The overall display features aWINDOWS organization, that is common general knowledge in computer art.Main window 60 shows a representation of the agents in two-dimensionalspace. Each agent is represented as a numbered dot. An action offollowing is indicated by a line drawn between the agent followed andthe agent following. If the two are very close to each other, theinterconnection becomes invisible. Clustering is a result of suchfollowing. All agents of a cluster are connected along branches of atree that has a particular root item. Motion is shown by differencebetween successive images. The behaviour of the agents is determined bywhat the database offers to the user.

A few control buttons are deemed self-explanatory. Secondary windows 62and 64 give metrics Coverage and Precision to be discussed hereinafter.Main control window 66 represents the user control interface for thesystem. First, field 76 shows the details of the item that is presentlypresented as audio, in this case being limited to song title, composer,and principal performer. Furthermore, seven control buttons are shownthat are largely conventional, to wit play, stop, pause, previous, next,fast rewind and fast forward: these features are based on theinstantaneous situations of the agents in the database, on which thecontrol system has decided a sequence of presenting the items to theuser person. The embodiment has four further control buttons. First,button 74 activates full random selection among the items in thedatabase, according to the shuffle play feature discussed supra. Second,button 72 activates the system according to the present invention,wherein the sequence among the items presented is controlled on thebasis of the agents. The third control mode is linearly along the listof items. Button 68 enters a positive evaluation of the item presentlybeing reproduced, whereas button 70 enters a negative evaluation: theseare used as inputs for the ID-3 policy to be discussed hereinafter.

SIMILARITY

Hereinafter, the determining of the similarity measure is discussed; thesimilarity has the nature of a descriptor. When track (item) i observestrack j, it calculates a similarity measure defined as the weighted sumof the corresponding attribute-value similarities between the two tracksaccording to: ##EQU1## Herein, K is the total number of attributes,s_(ijk) is the similarity between two tracks i,j for the listedattribute k, and w_(ik) is the associated weight factor. Weight factorsneed not be commutative. The weights are adjusted during adapting toevaluation signals from a user. Note that the observing action need notbe commutative either.

The calculation of the similarity s for attribute k is given by the datatype of the object class to which k belongs. The similarity ofattributes representing binary, nominal, ordinal, or interval data typesis 1 at identity, and 0 otherwise. The similarity of set-orientedattributes is determined by the weighted number of matching attributes.For rational attributes, the similarity is

    s.sub.ijk =1-{R.sub.k.sup.-1 *|v.sub.ik -v.sub.jk |},

wherein v_(ik) are the values for the two tracks for attribute k andR_(k) is the maximum rank difference or total span for that attribute.

By itself, musical preference to attribute value pairs is mapped bymeans of the ID3 procedure, that has been described in J. R. Quinlan,Induction of Decision Trees, Machine Learning 1, p.81-106, 1986. Thisprocedure shows which attributes are most accurate in distinguishingpreferred from non-preferred musical tracks. A so-called parent set oftracks is during a training session labelled by the user as preferred ornon-preferred. Next, this parent set is partitioned into disjointsubsets until all subsets consist of tracks that either are all labelledpreferred, or all are labelled non-preferred. This partitioning isrecursively directed along attributes. At each recursion, a so-calledbest attribute is chosen. In the embodiment, this was the attribute thatgave the highest entropy reduction (highest information gain) for thepartitioning. This entropy measure expresses how well the associatedattribute distributes the parent set along its values into uniformlylabelled subsets. The entropy is defined as:

    E(S)=-(p/N)*log(p/N)-{(N-p)/N}*log{(N-p)/N}

Herein, the parent set S contains N tracks, of which p are labelledpreferred. The entropy is maximum at 1 if exactly half of the tracks are`preferred`. The information gain with respect to a parent set S andattribute A with the resulting disjoint subsets S_(i), wherein i=1. . .I is defined as: ##EQU2## Herein, |S| is the number of elements in S. Inthe resulting tree, each path from the root to a leaf specifies theorder wherein each successive attribute divides the parent set into auniformly labelled subset. After generation of the tree, the weightfactors of the track attributes are adjusted by passing each track ofthe listening session along the tree, while taking the appropriatebranch for each attribute value. The weight factor associated with eachattribute is multiplied by a constant, if the track is labelled as`preferred`, and divided by a constant otherwise.

For monitoring the performance of the system, herein the exemplaryquantities performance and coverage are considered, cf. FIG. 3, smallinsert windows 62, 64. For better explanation, reference is had to FIG.10. This figure presumes a large collection of items, denoted as Ω, isat one's disposal. The subset relevant to one's preference is denoted asthe yet unknown set P. Consider a sequence of programmes presentedsuccessively in time starting at time t=1. Participants indicatepreferred and rejected items in each programme. Now, a programmepresented at some time t=T, denoted as C(T), will have a subset ofpreferred items, denoted as C_(p) (T), which falls within the region ofP.

Precision measures the fraction of preferred items in a programmepresented at time t=T, ##EQU3## where the operator #(C) denotes thenumber of elements in a programme C.

Coverage indicates how many different preferred tracks are selected overall programmes, and hence indicates the variety between programmes thatcan be attained within P. ##EQU4## Over time, the coverage measure is anon-decreasing curve and ideally approaches 1, meaning that a nearlycomplete coverage of P has occurred. Its definition however requires apriori knowledge of the set P, which is rather imaginary with user testsin contrast with simulations. For this reason, the normalizingdenominator in the latter Equation has been left out. In spite of theabsence of this normalization, one is still able to explain phenomena bythe course and trend of the coverage curve. Note that upon presentationof a new CD disc, its seed data must be downloaded; likewise, uponphysically removing a disc from the system, the data associated theretoare deleted. Therefore, the precision does not inform us aboutalternative sets of preferred tracks that could have been selected. Aparticular strategy is to offer to the user not only a large proportionof the preferred tracks, but also a wide variety among the trackslabelled as preferred. In this way, all favourite material in the musiccollection can be evenly spread over successive listening sessions. Theabove coverage metric closely resembles the recall metric used ininformation retrieval theory.

FIG. 4 gives a typical listening scenario of a system according to theinvention. In block 100, the system is initialized, the control softwareloaded, as well as the items that can be presented to a prospectiveuser. All this may have been done long ago. Moreover, the presentationsystem according to the invention can be initialized for subsequentactivation. This can be done through defining one ore more prototypicalusers that would have respective different behaviour and/or clusteringpatterns among the items. Another manner of providing seed-informationsfor the system can be by providing a uniform distribution in space andmore or less random dynamic values for the behaviour parameters.

In block 102 the power-on switch is activated. In block 104 the userperson enters a request for a personalized listening session. In block106 successive items are presented to the user in question, as shownmore in detail at right. In block 116 the request is entered. In block118, the successive items or tracks are effectively presented. In block122, the user person enters a rating or evaluating quantity to the itemjust presented. As discussed earlier, an elementary rating system isgood versus bad, but others are feasible. In block 120 standard usercommands for "repeat" or "skip" track are symbolized. In block 124 theuser requests to stop the sequence. In block 126 the sequence iseffectively stopped, and the system reverts to its main selection menu,or another less specific situation. This terminates the listeningsession according to the terminology of the present invention.Thereupon, this may terminate or freeze the interaction between thevarious tracks, until a new listening session according to the inventionis initiated. Alternatively, the interaction just proceeds. In block 108the user may request a random presentation sequence or another knownsequence. In block 110 various items are presented to the sequence nowchosen. In block 112 the power switch is interrupted. In block 114, thesession is stopped.

FIG. 5 is a detailed object diagram of a set of items. Block 130symbolizes the complete collection of tracks, that has included thefollowing standard database type features: initialize, load, save,number the tracks, add one track, get one track. Other features may beadded. Block 132 symbolizes a single track, and its attributes andcalculation of similarity with another track. It comprises a completetabular description from which an excerpt is available. The itemsounds-like is the name of a characteristic excerpt, the item noteallows for free-format entering of extra information for laterrepresentation.

Block 134 for one particular track represents its superclass of alldomain-type attributes that each have a name and a domain-typedescription. For a single attribute the block allows the calculation ofsimilarity with a corresponding attribute of another track. Block 136represents for each instance a float vector class that contains theweight factors for all attributes for similarity calculations. As shownin the lower part of the Figure, this Attribute class is furtherspecialized into more domain-specific Attribute classes. Accumulatorblock 138 maintains the number of occurrences of a particular object,such as a person or a particular musical instrument, in the database.

Block 140 represents `hard` attributes such as represented by blocks152-156; certain of these attributes may have a hierarchical structure,such as a particular theatre in a particular city. Such contents havebeen indicated by interrupted lines. Block 142 represents a set of suchobjects. Block 144 represents binary attributes such as indicated inblock 166. Block 146 represents nominal attributes, such as indicated inblocks 158-164, 168-174. Block 148 represents rational attributes suchas indicated. Each rational attribute has an instance variable `v` andcorresponding span 150 that gives minimum and maximum values for thesimilarity calculation.

FIG. 6 gives a detailed object diagram of item behaviour or populationdynamics. The central element is the track agent 200, each onerepresenting a single track. A track can have three different types ofbehaviour: 1) a free track does not form part of a cluster, and is notpart of a follow behaviour; 2) a follower follows another track, and maybe followed by a still further track; together they belong to onecluster; 3) a leader is followed by at least one other track of the samecluster. Furthermore, a track has various instance variables, to witposition x,y and velocity vx,vy in a space that for the embodiment hasbeen taken two-dimensionally, and further variables pace and curSim.Pace is a velocity range between 1 and 6 describing the effect ofdisliking or dissimilarity with another track. CurrSim is the currentsimilarity quantity f_(v) (s_(ij))=s_(ij) ^(Vmax-vi+1) on which thefollow behaviour is based. Herein, v_(i) is the current pace of thetrack, and s_(ij) has been defined elsewhere. The lowest part of block202 shows functions of a track that are self-explanatory. Block 206represents all tracks that are contained in the set of objects. Block202 represents a cluster that contains a selection of the items, has asvariable its number of tracks, and functions that may change its numberof tracks and their internal similarity. Block 210 represents aparticular track and may calculate a similarity quantity with anothertrack. Block 212 represents a sector of the two-dimensional spacewherein the tracks move, and block 214 the regular sector grid on whichthe sectors are defined. The sectors are used to discriminate thedistance between various tracks: only when in the same sector they maystart to sense each other. On the other hand, following behaviour maycross a border between two sectors. The representation of theinterconnections between the various blocks in this and subsequentFigures will be discussed with reference to FIG. 9.

FIG. 7 gives a detailed object diagram of interaction between trackitems and a human user person. Block 232 represents an interaction agentor algorithm, that may control any of the four listed operation modes.The present invention is active in the mode DoPATS, that itself may bedistinguished in a start and a stop facility. Pool 230 is a containerwith FIFO functionality for keeping the results of the latest listeningsessions; its maximum size may be determined as a listening duration, anumber of discrete sessions, a number of tracks activated, or acombination thereof. A Session 236 is a compilation of tracks aspresented to a user. It is a specialization of the class Clusterdescribed earlier. The size is upper-bounded, and the extract presentedfrom Cluster is determined by inter-track similarity. The Cluster 234 isthe total number of tracks that can be selected. Assessor 244 keepstrack of the performance of the inventive functionality and theunfolding of the emerging clusters by calculating various measures suchas precision, coverage, average intracluster similarity, and dynamicstate. The latter quantities were discussed supra. Monitor 246 and theset of all tracks 242 are part of a user interface. Track 240 is the onebeing currently presented to the user. Node 238 describes the actualnode.

FIG. 8 is a detailed object diagram of the so-called ID3 bookkeepingreferred earlier. This method builds a decision tree top-down bymeasuring at each interior node which attribute is currently the bestclassifier. The following object classes are identified: Node 238 (cf.FIG. 70) does the bookkeeping at a tree level necessary for the ID3procedure. The tree is made up from instances of Node. The set ofexample tracks classified up to this node is maintained by theassociation has₋₋ set. The association tests refers to the attributethat is chosen as best classifier for this node. The set of attributesthat have not yet been tested along the path from the root to this nodeis held by the association activeAttrs. Each attribute can only bechosen once as best attribute. Block 260 contains the numbers ofpositively and negatively labelled tracks. The association comprisesmaintains the attributes originating from the corresponding track. Thisclass replaces the class Track, because not all attributes and theirvalues need to be considered relevant for the ID3 procedure.

FIG. 9 shows graphical symbols used in the object diagrams of FIGS. 6-8.Therein, composite block 300 symbolizes an object that has a name (302),and some optional attributes with a name (304), and some optionalmethods with a name (306). Entry 308 represents a named association.Entry 310 represents a multiplicity (1,1). Entry 312 represents amultiplicity (0,1). Entry 314 represents a one-to-many multiplicity.Entry 316 represents a zero-to-many multiplicity. Entry 318 representsan is₋₋ a₋₋ relation or inheritance condition. Arrow 320 denotes aproducer-to-consumer data flow. The last entry represents an instancecreation, wherein object name (322) is of class Name (324).

We claim:
 1. A user-adaptive audio and/or video presentation systemcontaining a background presentation sub-system for presenting audioand/or video items, control means for controlling said presentationsub-system as regarding an automatically generated selection sequenceamongst said items according to a user preference pattern, and outputmeans for physically presenting selected items to a user,wherein eachsaid item has a uniformly structured first set of static attribute dataand associated second set of weight value data indicating relevance ofthe static attribute data, and also a third set of dynamic behaviourparameter data, and said items are with respect to their dynamicbehaviour collectively governed by a set of behavioral and interactivityprescriptions, so that each respective item represents a respectiveautonomous agent, and said control means include processing means forunder influence of said first and second sets of data and as governed bysaid prescriptions, updating said weights and dynamic behaviourparameter data of each item in question vis a vis other said items forsubsequently influencing said controlling.
 2. A system as claimed inclaim 1, wherein said behaviour is metaphored as motion within a finitespace of at least two dimensions.
 3. A system as claimed in claim 1, andbeing arranged for forming among said items one or more temporaryassociation clusters which forming is conditional to similar behaviouramongst the respective item agents in such cluster, and saidpresentation favours an item pertaining to a most recently accessed saidcluster.
 4. A system as claimed in claim 3, wherein associating to acluster is subject to arising of a sense₋₋ item situation, followed bydetected similarity in an observe₋₋ item situation.
 5. A system asclaimed in claim 1, wherein said updating is enabled for a particularsaid item independent of actual scheduling for presentation thereof. 6.A system as claimed in claim 1, and having user evaluation input meansfor selectively influencing said updating at least with respect to saidweight value data for a particular item through an evaluatory physicalinput.
 7. A system as claimed in claim 6, wherein said particular itemis the item presented most recently.
 8. A system as claimed in claim 6,wherein said influencing and updating is effective on a whole-clusterbasis.
 9. A system as claimed in claim 1, and having freeze means forblocking autonomy of said agents, and thus freezing said behaviour asdictated by said autonomy.
 10. A system as claimed in claim 1, for usein a multimedia environment.
 11. A system as claimed in claim 1, andallowing simultaneous access at a plurality of user stations.
 12. Asystem as claimed in claim 1, wherein said presentation sub-system is astorage system for storing and accessing audio items which aredistributed amongst a plurality of physical media instances that eachallow collective storage of a plurality of such items.
 13. A system asclaimed in claim 1, wherein said presentation system is a storage systemwith write feasibility for accommodating storage of said dynamicparameter data or other calculated data after formation of the latter.14. A method for user-adaptive audio and/or video presentationcomprising the steps of:selectively accessing a background presentationsub-system for presenting audio and/or video items according to a userpreference pattern, and physically outputting selected items to a user,wherein operating each such item as an autonomous agent on the basis ofa uniformly structured first set of static attribute data and associatedsecond set of weight value data indicating relevance of static attributedata, and also a third set of dynamic behaviour parameter dataassociated to the item, and furthermore through behavioral andinteractivity prescriptions for governing their collective dynamicbehaviour, and by under influence of said first and second sets of dataand as governed by said prescriptions, updating said weights and dynamicbehaviour parameter data of each item in question vis a vis other saiditems for subsequently influencing said controlling.